Body posture determination methods have many applications, including product design, ergonomic workplace design, human body simulation, virtual reality, and animation industry. Initiated in robotics, inverse kinematic (IK) method has been widely applied to proactive human body posture estimation. The analytic inverse kinematic (AIK) method is a convenient and time-saving type of IK methods. It is also indicated that, based on AIK methods, a specific body posture can be determined by the optimization of an arbitrary objective function. The objective of this paper is to predict the postures of human arms during reaching tasks. In this research, a human body model is established in MATLAB, where the middle rotation axis analytic kinematic method is accomplished, based on this model. The joint displacement function and joint discomfort function are selected to be initially applied in this AIK method. Results show that neither the joint displacement function nor the joint discomfort function predicts postures that are close enough to natural upper limb postures of human being, during reaching tasks. Therefore, a bi-criterion objective function is proposed by integrating the joint displacement function and joint discomfort function. The accuracy of the arm postures, predicted by the proposed objective function, is the most satisfactory, while the optimal value of the coefficient, in the proposed objective function, is determined by golden section search.

When designing programs to assist the poor, it is important to recognize who is most in need of government assistance. Although measures of poverty are often based on income alone, poverty measures based on both income and assets provide greater precision in the analysis of this group since accumulated assets can be liquidated to compensate for temporary shortfalls in income. The current study used the Panel Study of Income Dynamics (2007–2017) to analyze associations between different facets of poverty dynamics (i.e. poverty entry and exit) and its determinants. We explored differences in results based on whether poverty was measured by income alone, or income plus assets. The Cox proportional hazard regression was used to examine how demographic characteristics predicted poverty entry and poverty exit. Results indicated factors predicting poverty entry were not identical to those predicting difficulty of exiting poverty. Also, the risk of poverty entry and exit differed based on whether poverty was measured by income alone, or income plus assets. Thus, using income plus assets provides new perspectives into poverty dynamics which past research, based on income alone, did not provide. These new insights can be used to inform decisions about policies for poverty prevention and alleviation.

Business process management (BPM) has been the main driver behind company optimization and operational efficiency. However, the digitization era we live in necessitates that organizations be agile and adaptable. Delivering unprecedented rates of automation-fueled agility is necessary to be a part of this digital revolution. On the other hand, BPM automation cannot be done only by concentrating on procedure space and traditional planning methodologies. With the introduction of BPM, where the deployment of BPM with cloud computing has undergone enormous development lately, cloud computing has been considered a particularly active topic of study. Cloud computing points to the provision of dependable computing environments based on improved infrastructure availability and service quality without imposing a significant cost load. This research aims to discover the relationship between technical factors, financial factors, environmental factors, security of the cloud-based information systems, and the agile development of industrial BPM (IBPM). The present study aims to fill this gap and show how partial least squares structural equation modeling (SEM) can be employed in this field. Importance–performance map analysis (IPMA) evaluated the importance and performance of factors in the SEM. IPMA enables the identification of factors with relatively low performance but relatively high importance in shaping dependent variables. The empirical findings showed that four key factors (technical, financial, environmental, and security) positively influence the agile development of IBPM.

Maternal experiences of childhood adversity can increase the risk of emotional and behavioural problems in their children. This systematic review and meta-analysis provide the first narrative and quantitative synthesis of the mediators and moderators involved in the link between maternal childhood adversity and children's emotional and behavioural development. We searched EMBASE, PsycINFO, Medline, Cochrane Library, grey literature and reference lists. Studies published up to February 2021 were included if they explored mediators or moderators between maternal childhood adversity and their children's emotional and behavioural development. Data were synthesised narratively and quantitatively by meta-analytic approaches. The search yielded 781 articles, with 74 full-text articles reviewed, and 41 studies meeting inclusion criteria. Maternal mental health was a significant individual-level mediator, while child traumatic experiences and insecure maternal–child attachment were consistent family-level mediators. However, the evidence for community-level mediators was limited. A meta-analysis of nine single-mediating analyses from five studies indicated three mediating pathways: maternal depression, negative parenting practices and maternal insecure attachment, with pooled indirect standardised effects of 0.10 [95% CI (0.03–0.17)), 0.01 (95% CI (−0.02 to 0.04)] and 0.07 [95% CI (0.01–0.12)], respectively. Research studies on moderators were few and identified some individual-level factors, such as child sex (e.g. the mediating role of parenting practices being only significant in girls), biological factors (e.g. maternal cortisol level) and genetic factors (e.g. child's serotonin-transporter genotype). In conclusion, maternal depression and maternal insecure attachment are two established mediating pathways that can explain the link between maternal childhood adversity and their children's emotional and behavioural development and offer opportunities for intervention.

COVID-19 is erupting globally and Wuhan successfully controlled it within a month. Infections arose from infectious persons outside hospitals. After data revision, data-based and model-based analyses are implemented and the conclusions are as follows. The incubation period of most infected people may be 6-7 days. The number of infectious persons outside hospitals in Wuhan on Jan.20 is about 10000 and reached more than 20000 on the day of Lockdown, it exceeded 72000 on Feb.4. Both data-based and model-based analyses gave out the evolution of the reproduction number, which is over 2.5 in early January, then go down to 1.62 in late January and 1.20 in early February, a sudden drop to less than 0.5 due to the strict Stay-at-home management after Feb.11. Strategies of Stay-at-home, Safe-protective measures and Ark hospitals are the main contributions to control COVID-19 in Wuhan. Two inflection points of COVID-19 in Wuhan exactly correspond to Feb.5 and Feb.15, the two days when Ark hospitals were introduced and the complete implementation of Stay-at-home. Based on the expression of the reproduction number, group immunity also is discussed. It shows that only when the group immunization rate is over 75 percent can COVID-19 be under control, group immunity actually would be full infection and the total deaths will be 220,000 for a city as big as Wuhan. Sensitivity analysis suggests that 30 percent of people staying at home in combination with better behavior changes, such as social-distancing and frequent hand-washing, can effectively contain COVID-19. But only when this proportion is over 60 percent can the control effect and efficiency like Wuhan be obtained.

N-acetylcysteine (NAC) possesses a strong capability to ameliorate high-fat diet (HFD)-induced nonalcoholic-fatty liver disease (NAFLD) in mice, but the underlying mechanism is still unknown. Our study aimed to clarify the involvement of long noncoding RNAs (lncRNAs) in the beneficial effects of NAC on HFD-induced NAFLD. C57BL/6J mice were fed a normal-fat diet (10 % fat), a HFD (45 % fat), or a HFD plus NAC (2 g/L in the drinking water). After 14-week of intervention, NAC obviously rescued the deleterious alterations induced by HFD, including the changes in body and liver weights, hepatic triglycerides (TGs), plasma alanine aminotransferase (ALT), plasma aspartate transaminase (AST), and liver histomorphology (H&E and Oil red O staining). Through whole-transcriptome sequencing, 52167 (50758 known and 1409 novel) hepatic lncRNAs were detected. Our cross-comparison data revealed the expression of 175 lncRNAs were significantly changed by HFD but reversed by NAC. Five of those lncRNAs, lncRNA-NONMMUT148902.1 (NO_902.1), lncRNA-XR_001781798.1 (XR_798.1), lncRNA-NONMMUT141720.1 (NO_720.1), lncRNA-XR_869907.1 (XR_907.1), and lncRNA-ENSMUST00000132181 (EN_181) were selected based on an absolute log2 fold change (FC) value of greater than 4, P-value < 0.01, and P-adjusted value < 0.01. Further qRT-PCR analysis showed that the levels of lncRNA-NO_902.1, lncRNA-XR_798.1, and lncRNA-EN_181 were dramatically decreased by HFD but restored by NAC, consistent with the RNA-sequencing. Finally, we constructed a ceRNA network containing lncRNA-EN_181, 3 miRNAs, and 13 mRNAs, which was associated with the NAC-ameliorated NAFLD. Overall, lncRNA-EN_181 might be a potential target in NAC-ameliorated NAFLD. This finding enhanced our understanding of the biological mechanisms underlying the beneficial role of NAC.

Drawing on social learning theory and taking a motivational perspective, this study mainly investigates how leader humility can promote employees' other-oriented motivations, and uncovers the other-serving motivational mechanism through which leader humility can impact their employees' different types of voice behavior. By collecting data from 152 leader–subordinate dyads through an online survey, the results revealed that leader humility was positively related to both employees' prosocial motivation and organizational concern motivation. Meanwhile, these two motivations play mediating roles in explaining how leader humility can positively affect employees' supportive voice and challenging voice. It is noteworthy that leader humility, which features highlighting the value and strength of others, is more likely to trigger employees' prosocial motivation and thus influence their voice behavior. This research extends our understanding of leader humility, employee motivation, and workplace voice. Practical implications and limitations of the results are also discussed.

The impacts of training image sizes and optimizers on deep convolutional neural networks for weed detection in alfalfa have not been well explored. In this research, AlexNet, GoogLeNet, VGGNet, and ResNet were trained with various sizes of input images, including 200 × 200, 400 × 400, 600 × 600, and 800 × 800 pixels, and deep learning optimizers including Adagrad, AdaDelta, Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD). Increasing input image sizes reduced the classification accuracy of all neural networks. The neural networks trained with the input images of 200 × 200 pixels resulted in better classification accuracy than the other image sizes investigated here. The optimizers affected the performance of the neural networks for weed detection. AlexNet and GoogLeNet trained with AdaDelta and SGD outperformed Adagrad and Adam; VGGNet trained with AdaDelta outperformed Adagrad, Adam, and SGD; and ResNet trained with AdaDelta and Adagrad outperformed the Adam and SGD. When the neural networks were trained with the best-performed input image size (200 × 200 pixels) and the deep learning optimizer, VGGNet was the most effective neural network with high precision and recall values (≥0.99) in the validation and testing datasets. At the same time, ResNet was the least effective neural network for classifying images containing weeds. However, the detection accuracy did not differ between broadleaf and grass weeds for the different neural networks studied here. The developed neural networks can be used for scouting weed infestations in alfalfa and further integrated into the machine vision subsystem of smart sprayers for site-specific weed control.